ArticleBiotechnology reports (Amsterdam, Netherlands)2019
Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition.
Article in Biotechnology reports (Amsterdam, Netherlands), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Article
- Mass transfer in heterogeneous biofilms: Key issues in biofilm reactors and AI-driven performance prediction.Environmental science and ecotechnology · 2024Review
- Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces.Antibiotics (Basel, Switzerland) · 2024Review
- Influence of training and expertise on deep neural network attention and human attention during a medical image classification task.Journal of vision · 2024Article
- Machine learning identification of Pseudomonas aeruginosa strains from colony image data.PLoS computational biology · 2023Article
- High-precision density mapping of marine debris and floating plastics via satellite imagery.Scientific reports · 2023Article
- Quantum deep learning by sampling neural nets with a quantum annealer.Scientific reports · 2023Article
- Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective.Journal of Alzheimer's disease : JAD · 2023Review
- Progress in bioleaching: fundamentals and mechanisms of microbial metal sulfide oxidation - part A.Applied microbiology and biotechnology · 2022Review
- Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions.Animals : an open access journal from MDPI · 2022Article
- Why Machine Learning Should Be Taught in Medical Schools.Medical science educator · 2022Article
- Biofilms in Surgical Site Infections: Recent Advances and Novel Prevention and Eradication Strategies.Antibiotics (Basel, Switzerland) · 2022Review
- An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms.Frontiers in microbiology · 2022Article
- Does Machine Understanding Require Consciousness?Frontiers in systems neuroscience · 2022Article
- The Two Weapons against Bacterial Biofilms: Detection and Treatment.Antibiotics (Basel, Switzerland) · 2021Review
- Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB.BMC molecular and cell biology · 2021Article
- Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation.Scientific reports · 2021Article
- Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory.Sensors (Basel, Switzerland) · 2021Article
- Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks.Frontiers in microbiology · 2021Article
- Deep Regression Prediction of Rheological Properties of SIS-Modified Asphalt Binders.Materials (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases.
methodsThe ability of deep neural networks
resultsA low number of microscopy images per category (<600) was sufficient for highly efficient computational analysis of the biofilm's bacterial composition. The use of deep neural networks reached an accuracy of classification of ∼90% compared to ∼50% for human experts.
conclusionsDeep neural networks outperform human experts' capacity in characterizing bacterial biofilm composition involved in the degradation of chalcopyrite. This approach provides an alternative to standard, time-consuming biochemical methods.
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